Development of a median sternotomy simulation model for cardiac surgery training
Bibliographic record
Abstract
OBJECTIVE: We sought to develop a simulation model to train resident physicians in the performance of a median sternotomy. METHODS: A modified Delphi consensus process was used with cardiac surgery staff to develop a 20-point checklist for the safe performance of a median sternotomy. Thirteen junior cardiac surgery trainees from across Canada participated in this study to assess the simulation model. Trainees performed the sternotomy before and after reviewing an instructional video. Two senior cardiac surgery resident physicians assessed the participants with the checklist during each session. An entry and exit questionnaire was given to the participants to evaluate the simulation model. RESULTS: = .003). The checklist interrater reliability was κ = 0.47 (moderate) for before training and κ = 0.37 (fair) for after training. All study participants rated the simulation sessions as very useful or extremely useful. CONCLUSIONS: Using the simulation model, training video, and checklist, trainees were able to improve their skill in performing a median sternotomy. This improvement was associated with longer times to complete all procedure steps. Rater training may further improve interrater reliability. Our median sternotomy checklist and simulation model can be adopted for the technical skills training of future cardiac surgery trainees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".